A local-first prompt engineering powerhouse that transforms vague queries into high-precision, structured AI instructions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prompt-optimizer-pro
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install prompt-optimizer-pro using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Prompt Optimizer?
The Prompt Optimizer is an essential utility designed for users who want to extract maximum performance from large language models. Built as a sophisticated part of the Openclaw Skills ecosystem, it functions as a local expert in prompt engineering, applying techniques like chain of thought, few-shot prompting, and role assignment to turn weak inputs into powerful instructions.
This skill is entirely model-agnostic, meaning it works effectively whether you are targeting Claude, GPT, Gemini, or local models like Llama and Mistral. Because the Prompt Optimizer prioritizes security, all your prompt libraries, optimization history, and usage statistics remain on your local machine with no external API calls required for the optimization logic itself.
Prompt Optimizer Use Cases
- Turning vague requests like "write a blog" into comprehensive, multi-step content briefs.
- Diagnosing and fixing "bad responses" by identifying lack of context or missing constraints.
- Generating complex system prompts for custom AI agents or customer support bots.
- Building few-shot example sets to ensure AI models follow specific classification or formatting rules.
- Creating mega prompts that combine role-play, PAS frameworks, and strict output constraints for marketing and coding tasks.
How Prompt Optimizer Works
- The user triggers the skill by asking to improve, optimize, or create a specific type of prompt.
- The skill analyzes the request against professional prompt engineering standards, checking for specificity, audience, tone, and structure.
- It generates an optimized version of the prompt, often providing a before-and-after comparison and a percentage-based clarity improvement score.
- Users can refine the result further by requesting specific formats, such as JSON or markdown tables.
- The optimized prompt can be saved to a local library for instant retrieval in future sessions.
Prompt Optimizer Setup
To get started with this skill in your Openclaw Skills environment, you need to create the local data directory to store your library and history:
mkdir -p ~/.openclaw/prompt-optimizer
You should also initialize your local configuration files to ensure stats and saved prompts are persisted correctly. The skill will automatically handle reading and writing to this directory as you optimize your workflow.
Prompt Optimizer Data Schema & Taxonomy
The Prompt Optimizer organizes data locally to maintain privacy and performance. The schema includes the following files stored in ~/.openclaw/prompt-optimizer/:
| File | Purpose | Key Data Points |
|---|---|---|
settings.json |
Configuration | Optimization counts, usage streaks, and default model preferences. |
library.json |
Prompt Storage | Categorized list of saved prompts (Writing, Coding, Analysis, Creative). |
history.json |
Audit Log | A record of previously optimized prompts for iterative reference. |
metadata |
Skill Config | Defines tool requirements such as read and write permissions for the local filesystem. |
Prompt Optimizer Advanced Features
- Prompt Scoring: A numerical evaluation (0-100) of your prompt's effectiveness based on six specific criteria.
- A/B Comparison: Compare two different prompt variations to determine which is likely to yield better results.
- Negative Prompting: Automatically adds constraints to prevent AI from using cliches or hallucinations.
- Chain of Thought Builder: Generates structured reasoning paths to improve the accuracy of complex analytical tasks.
- Model-Specific Tweaks: Provides specialized optimization tips for Claude (using XML tags) versus GPT or Gemini.
SKILL.md
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